Computer implemented systems and methods for improving renewable energy systems performance guarantees

ABSTRACT

Systems and methods are provided for collecting, aggregating, and analyzing data associated with the installation and deployment of systems. Energy systems, ( 300 ) specifically renewable energy generation systems, are used as examples. The aggregated data serve as the basis for a variety of services that facilitate the adoption and deployment of these systems. Services are provided that aid in the modeling and establishment of improved System Performance Guarantee commitments. Additionally, services are provided that improve the system performance, improve the installation, lower the cost, and provide monitoring and service to maintain improved performance.

FIELD OF THE INVENTION

In general, the present invention relates to computer implemented systems and methods for providing services to a network of customers, more specifically to services enabled by methods comprising the collection, aggregation, and analysis of data in a central database from a plurality of systems that are not otherwise associated with one another to provide performance metrics and most particularly to the establishment and improvement of various performance metrics related to the execution of customer activities and the initiation of specific actions related to performance in comparison with such metrics. More specifically, the present invention relates to computer implemented services enabled by systems and methods comprising the collection, aggregation, and analysis of data related to the installation and operation of renewable energy systems comprising solar energy, wind turbine energy, tidal energy, geothermal energy, and the like, or to distributed energy generation systems comprising waste-to-energy generation systems, fuel cells, microturbines, diesel generators, and the like.

BACKGROUND OF THE INVENTION

There is increased interest in the development and deployment of renewable energy systems comprising solar energy, wind turbine energy, tidal energy, geothermal energy, and the like, or to distributed energy generation systems comprising waste-to-energy generation systems, fuel cells, microturbines, diesel generators, and the like. This interest is being driven by a number of factors including a limited supply of fossil fuels, increased pollution from the acquisition and use of fossil fuels, global warming considerations, rising costs of fossil fuels, the loss of natural lands due to the construction of fossil fuel power plants, continued utility grid degradation and blackouts, unpredictable energy prices, the need for local power generation in disaster recovery situations, the need to move away from centralized power plants to distributed energy systems for homeland security, and the like. Advancements in the development of renewable energy and distributed energy generation technologies have overcome earlier impediments such as poor efficiency, installation difficulty, high cost, high maintenance, and the like and are presently offering increasingly attractive alternatives to fossil fuel power systems in the generation and delivery of electric power.

One of the issues faced by the renewable energy and distributed energy generation industries is that the adoption and deployment of such systems is often sporadic and not well coordinated. The decision to invest in and install a renewable energy or distributed energy generation system is typically made at the individual entity level rather than as a planned activity for an entire community. For economy of language, in this context, an “entity” may comprise an individual, a company, an office building, a shopping mall, a shopping center, a sports complex, or other such organization, business, or group investing collectively in a source of energy. Consequently, the renewable energy and distributed energy generation industries often lack the coordinated, integrated infrastructure that is typically common in other industries. The lack of infrastructure inhibits the adoption and installation of new renewable energy and distributed energy generation systems and does not allow these industries to gain advantages due to cooperation or economies of scale to lower costs, increase acceptance and deployment, and attract additional investment capital.

Accordingly, there is a need for further developments in methods and systems to facilitate the connection and cooperation of the wide variety of entities and individual implementations of renewable energy or distributed energy generation systems to improve efficiencies, lower costs, facilitate new services, facilitate management and improvement of the energy production and distribution system as a whole, facilitate and improve training and education, facilitate energy commerce, and the like. In particular, there is a need for improved systems and methods to improve the guarantees made to End Users or other Customers based on aggregated data from a plurality of previously installed systems.

BRIEF SUMMARY OF THE INVENTION

Advancements in the development of renewable energy and distributed energy generation systems have overcome, to a large extent, earlier impediments such as poor efficiency, installation difficulty, high cost, high maintenance, and the like. Specifically, advancements in the technology associated with the capture and conversion of solar energy into useable electricity has led to an increased adoption and deployment rate of solar energy generation systems. However, the infrastructure associated with collecting and analyzing data associated with the distribution infrastructure, system performance, system response, system efficiency, costs, savings associated with the system, and the like has not grown at the same pace as the implementation of solar energy generation systems. Systems and methods for the collection, aggregation, and analyzing of this data and providing services based on the results of the analysis have been developed as part of some embodiments of the present invention.

In some embodiments of the present invention, the data collection systems and methods cited above may use a local communications device installed at the site of the renewable energy generation or distributed energy generation system to collect data on the system comprising system ID, location, performance, calibration, ambient conditions, efficiency, temperature, wind speed, wind direction, solar irradiance, energy generation, device status flags, and the like. Typical data collection systems comprise embedded sensors, external sensors, embedded computers, and the like. Typical local communications devices comprise modems, routers, switches, embedded computers, wireless transmitters, and the like. The data may be transmitted via a wireless or hardwired network or other communication means to a secure, central database where the data is aggregated with data from other systems and analyzed to provide value added services to the members of the renewable energy or distributed energy generation supply chain. Examples of suitable networks comprise the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a wireless network, cellular networks (e.g., GSM, GPRS, etc.), combinations thereof, and the like. Various embodiments of the present invention include security features such that proprietary or business-sensitive data is not accessible among different business entities, thereby providing all entities access to aggregated information while compromising the security of none.

Various embodiments of the present invention relate generally to systems and methods that utilize the secure, centrally collected, aggregated, and analyzed data to provide a number of beneficial services. The services may be desirable and useful to many “Supply Chain Entities” within the renewable energy or distributed energy generation system supply chain. For economy of language, we use the term, Supply Chain Entity or Entities to refer to one or more of the “Installation Technician”, the “Value Added Reseller (VAR)”, the “System Integrator”, the “Original Equipment Manufacturer (OEM)” component supplier, the “local energy utility”, various local government agencies, the Project Financier or Investor, the Distributed Utility provider, among others. These labels have been used for convenience in the context of the present teaching. It will be clear to those skilled in the art that those entities or parties that provide similar functions and services within the supply chain may use a wide variety of names and labels. These labels do not limit the scope of the present invention in any way.

In some embodiments of the present invention, the aggregated data may be used to offer services to the System Integrators that improve the offer of System Performance Guarantee commitments. The services may utilize a model of benchmark system performance established by the System Integrators or established from aggregated data based on system configuration details comprising OEM sub-modules, installation region, system orientation, system tilt angle, expected shading, system tracking features, system tracking capabilities, date, historical data, and the like. Typically, the System Integrators may select a coefficient between 0 and 1 to serve as a safety factor and calculates the System Performance Guarantee commitment as the multiplication of the model result and the coefficient. New system performance data may be collected and aggregated into the database and the System Performance Guarantee commitment may continue to improve over time as the benchmark system performance improves. The System Integrators may enjoy the benefits of improved performance guarantee accuracy, competitive differentiation by offering a higher performance guarantee, increased market share, increased End User satisfaction, and the like.

In some embodiments of the present invention, the aggregated data may be used to offer services to the other Supply Chain Entities that improve the offer of other kinds of System Performance Guarantee commitments. Examples of other kinds of System Performance Guarantee commitments comprise system installation costs, service warranties, OEM manufacturer parts warranties, and the like. The services may utilize a model of benchmark system performance established by the Supply Chain Entities or established from aggregated data based on system configuration details comprising OEM sub-modules, installation region, system orientation, system tilt angle, expected shading, system tracking features, system tracking capabilities, date, historical data, and the like. Typically, the Supply Chain Entities may select a coefficient between 0 and 1 to serve as a safety factor and calculates the System Performance Guarantee commitment as the multiplication of the model result and the coefficient. New system performance data may be collected and aggregated into the database and the System Performance Guarantee commitment may continue to improve over time as the benchmark system performance improves. The Supply Chain Entities may enjoy the benefits of improved performance guarantee accuracy, competitive differentiation by offering a higher performance guarantee, increased market share, increased End User satisfaction, and the like.

The methods of some embodiments of the present invention may be implemented on a plurality of systems. The systems may comprise one or more energy systems, sensors contained within the energy systems to monitor various settings and performance attributes of the energy systems, sensors associated with the energy systems to measure various environmental conditions, a communications device for managing two-way communications between the sensors, the energy systems, and a network, a network for transmitting the data to a centralized database, a centralized database for receiving and storing data from a plurality of systems, user interfaces for interacting with the centralized database, procedures for acting upon the data, and a plurality of output means for displaying the results of the procedure treatments.

BRIEF DESCRIPTION OF THE DRAWINGS

Other aspects, embodiments and advantages of the invention may become apparent upon reading of the detailed description of the invention and the appended claims provided below, and upon reference to the drawings in which:

FIG. 1 is a schematic representation of a portion of a typical renewable energy or distributed energy generation system supply chain.

FIG. 2 is a flow chart of steps in some embodiments of the present invention.

FIG. 3 is a schematic representation of a system pertaining to some embodiments of the present invention.

FIG. 4 depicts an illustrative computer system pertaining to various embodiments of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

In general, various embodiments of the present invention relate to systems and methods that utilize secure, centrally collected, aggregated, and analyzed data to provide a number of beneficial services. The services may be desirable and useful to many Supply Chain Entities within the renewable energy or distributed energy generation system supply chain.

In some embodiments of the present invention, the systems and methods provide services to the various Supply Chain Entities in the renewable energy or distributed energy generation system supply chain. As an illustration, consider the supply chain structure illustrated in FIG. 1 wherein, large national Systems Integrators, 101, market and sell the renewable energy or distributed energy generation systems to End Users, 104. Typically, the System Integrators may design and oversee the installation and commissioning of the renewable energy or distributed energy generation systems. The System Integrators may contract with VARs, 102, who are local to the End Users and who may perform services comprising installation, service, upgrades, retrofits, and the like on behalf of the System Integrators. Furthermore, the VARs may employ a plurality of Installation Technicians, 103, who may perform services comprising installation, service, upgrades, retrofits, and the like on behalf of the VARs. OEM component suppliers, 100, may supply components to the System Integrators, 101, or the VARs, 102. These labels have been used for convenience in the context of the present teaching. It will be clear to those skilled in the art that those entities or parties that provide similar functions and services within the supply chain may use a wide variety of names and labels. These labels do not limit the scope of the present invention in any way.

In an exemplary embodiment of the present invention, the systems and methods may be applied to a solar energy generation system. However, the solar energy example does not limit the scope of the present invention in any way. The systems and methods described herein may be applied to any general system. Specifically, the systems and methods described herein may be applied to any general energy system such as an energy consumption system, an energy generation system, an energy storage system, combinations thereof, and the like. More specifically, the systems and methods described herein may be applied to any renewable energy generation comprising solar energy, wind turbine energy, tidal energy, geothermal energy, and the like, or distributed energy generation technology comprising waste-to-energy generation technologies, fuel cells, microturbines, diesel generators, and the like or any combination thereof. In the context of the present teaching, a system comprising more than one type of system as listed above will be designated a “hybrid” system.

Typically, the solar energy system may be installed by an Installation Technician following an established installation checklist. The system may be connected to a central database via a network. Examples of suitable networks comprise the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a wireless network, cellular networks (e.g., GSM, GPRS, etc.), combinations thereof, and the like. In this exemplary embodiment, System Identification Data are collected at the point of sale by the System Integrator or the VAR, said System Identification Data comprising, End User identification, system warranty information, system performance guarantee commitment information, expected system power output, and the like. The System Identification Data are static in time meaning that they may not generally change once established. The System Identification Data may be entered into the central database and serve as a unique identifier for the system. System Configuration Data are collected during the manufacture and testing of the system, said System Configuration Data comprising, system configuration with OEM component identification, system wiring details, system tracking features, system tracking capabilities and the like. The System Configuration Data are generally static in time meaning that they may not generally change once established. However, the System Configuration Data may change during periods of service, upgrades, or enhancements to the system. The System Configuration Data may be entered into the central database and associated with the unique System Identification Data previously entered. System Installation Data are collected at the time of installation, said System Installation Data comprising, VAR identity, Installation Technician identity, installation region, system orientation, system tilt angle, expected shading, time to complete the system installation, number of errors during the system installation, an End User satisfaction index (EUSI), firmware revision, system parameter settings, and the like. In the context of the present teaching, “expected shading” may be associated with the area and time that the system is covered by shadows due to neighboring trees, building, structures, etc. It may be expressed in units of % coverage per hour for each time period of interest comprising months, seasons, years, billing periods, and the like. This quantity may be useful in estimating the performance of the system. The System Installation Data are static in time meaning that they may not generally change once established. The System Installation Data may be entered into the central database and associated with the unique System Identification Data previously entered. System Performance Data and ambient condition data are collected and continuously at a predefined interval after start-up of the system, said System Performance Data comprising, system response, system performance, ambient temperature, solar irradiance, conversion efficiency, current tilt angle, system energy output, current firmware revision, current system parameter settings, device fault and error codes, power, voltage, cumulative energy generated, and the like. The System Performance Data change with time and are entered into the central database as a time series with associated date and time stamps. The temporal System Performance Data are associated with the unique System Identification Data previously entered. The data correlated to the installation region may be aggregated to several levels of granularity, said levels comprising country, time zone, state or province, county, postal code, Global Positioning System (GPS) coordinates, and the like. Additionally, System History Data may be associated with each unique System Identification Data record. The System History Data captures changes in the System Configuration Data over time. Examples of System History Data comprise time-to-first-service-call, details of the service calls, steps taken to resolve the issues in the service calls, upgrades to the system configuration, new firmware revisions, new parameter settings, and the like. Entries in the System History Data typically contain date and time stamps so that changes may be tracked over the life of the system.

In some embodiments of the present invention, the systems and methods may be applied to a solar energy generation system as an example. Services may be provided to the System Integrators to improve the offer of System Performance Guarantee commitments. The services may utilize a model of benchmark system performance established by the System Integrators. The System Integrators may typically have a model that has been developed over time for use in their engineering design groups that they prefer. Alternatively, the services may utilize a model established from aggregated data from the centralized database based on system configuration details comprising OEM sub-modules installation region, system orientation, system tilt angle, system tracking features, system tracking capabilities, date, historical data, and the like. The services may use historical weather data from the centralized database as well as archived weather data to estimate the performance for the modeled system for all seasons of the year using Typical Meteorological Year (TMY) values for factors such as solar irradiance, number of rainy days, periods of low light, and the like. This a particularly advantageous aspect of some embodiments of the present invention because the system response and system performance may vary during the different seasons of the year due to differences in solar irradiance, temperature, number of cloudy days, typical cloud cover types and the like. The services allow the System Integrators to account for this variation in determining the System Performance Guarantee commitments.

Typically, the System Integrators may select a safety factor comprising a coefficient between 0 and 1. The System Performance Guarantee commitment to the End User for system response and system performance may be calculated by the multiplication of the result of the model and the coefficient. After the system is installed, calibrated, and begins operation, the system performance may be compared to the modeled performance to verify conformance. As mentioned previously, the variations due to weather and the different seasons may be included. The services may aggregate and normalize the system performance data for a plurality of systems. If the performance of the aggregation is consistently better than the model, the model may be enhanced or coefficient increased so that the System Integrators may improve their System Performance Guarantee commitments with confidence and achieve greater market share.

In some embodiments of the present invention, the aggregated data may be used to offer services to the other Supply Chain Entities that improve the offer of other kinds System Performance Guarantee commitments. Examples of other kinds of System Performance Guarantee commitments comprise system installation costs, service warranties, OEM manufacturer parts warranties, and the like. The services may utilize a model of benchmark system performance established by the Supply Chain Entities or established from aggregated data based on system configuration details comprising OEM sub-modules, installation region, system orientation, system tilt angle, expected shading, system tracking features, system tracking capabilities, date, historical data, and the like. Typically, the Supply Chain Entities may select a coefficient between 0 and 1 to serve as a safety factor and calculates the System Performance Guarantee commitment as the multiplication of the model result and the coefficient. The Supply Chain Entities may now accomplish this with confidence since the benchmarks may be established from a large database of actual values. New system performance data may be collected and aggregated into the database and the System Performance Guarantee commitment may continue to improve over time as the benchmark system performance improves. The Supply Chain Entities may enjoy the benefits of improved performance guarantee accuracy, competitive differentiation by offering a higher performance guarantee, increased market share, increased End User satisfaction, and the like.

In some embodiments of the present invention, the methods and procedures for using a model of benchmark system response and system performance to generate an expected result of system performance, selecting a coefficient between 0 and 1, calculating a guarantee by multiplying the model result by the coefficient, and monitoring the system performance and comparing it to the guarantee may follow the steps, 200-206, as outlined in FIG. 2. These exemplary steps are not meant to limit the scope of the present invention.

Through the services provided, the data may be manipulated and parsed by the various System Integrators subject to various security measures as discussed below. A plurality of standard procedures exists to aid in the manipulation of the data. Examples of suitable procedures comprise methods for calculating typical statistical values such as mean, median, average, standard deviation, maximum value, minimum value, variance, and the like. These procedures are listed as illustrations only and do not limit the scope of the present invention in any way. Alternatively, the System Integrators may develop and generate a custom procedure to extract and manipulate the data for their specific purpose.

The systems and methods may include a number of security measures to protect the intellectual property and confidential information for the various Supply Chain Entities of the renewable energy system supply chain. The security measures comprise software passwords, tokens, smart cards, biometric identification means, and the like. The security measures ensure that any specific System Integrator, VAR, or OEM manufacturer is only allowed access to the detailed data generated by systems under their specific responsibility. However, the System Integrators, VARs, or OEM manufacturers may request results based on the analysis of the aggregated data across the database so that they may compare their data to the larger population of systems.

The database may contain data from systems installed worldwide by a large number of Supply Chain Entities. The different pattern fill of the circles representing systems, 300, illustrated in FIG. 3 is meant to convey that these systems are associated with different Supply Chain Entities. Comparisons and analyses may be completed by aggregating data from systems with similar features comprising System Integrator ID, VAR ID, Installation Technician ID, expected system power output, system configuration with OEM component identification, system wiring details, system tracking features, system tracking capabilities, expected shading, installation region, system orientation, system tilt angle, firmware revision, system parameter settings, system response, system performance, ambient temperature, solar irradiance, conversion efficiency, current tilt angle, system energy output, device fault and error codes, power, voltage, cumulative energy generated, and the like. Advantageously, the database enables the Supply Chain Entities to compare detailed data across systems under their responsibility or to compare their data to benchmark or aggregated data across the entire database. For example, a System Integrator may compare detailed data for his systems installed across a large region such as North America. Alternatively, the same System Integrator may compare data for one or more of his systems with benchmark or aggregated data for systems installed in a completely different region such as Europe.

Referring now to FIG. 3, the methods of some embodiments of the present invention may be implemented on a plurality of systems. The systems may comprise one or more energy systems, 300, sensors contained within the energy system to monitor various settings and performance attributes of the energy system, sensors associated with the energy system to measure various environmental conditions, 302, a local communications device for managing two-way communications between the sensors, the energy systems, and a network, 303, a network for transmitting the data to a centralized database, 304, a centralized database for receiving and storing data from the plurality of systems, 305, user interfaces for interacting with the centralized database, 306-309, procedures for acting upon the data, and a plurality of output devices for displaying the results of the procedure action, 306-310.

Continuing to refer to FIG. 3, in some exemplary embodiments comprising solar energy generation systems, the sensors contained within the system may monitor various settings and performance attributes comprising, system response, system performance, conversion efficiency, current tilt angle, system energy output, current firmware revision, current system parameter settings, device fault and error codes, power, voltage, cumulative energy generated, and the like. Sensors associated with the system, 302, may measure various environmental conditions comprising ambient temperature, solar irradiance, and the like. The data may be communicated onto a network, 304, by a local communications device, 303. Examples of suitable networks comprise the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a wireless network, cellular networks (e.g., GSM, GPRS, etc.), combinations thereof, and the like. The data may be received and stored on a centralized database, 305. The data in the centralized database may be accessed by a plurality of user interfaces comprising computer terminals, 307, personal computers (PCs), 306, personal digital assistants (PDAs), 308, cellular phones, 309, interactive displays, and the like. This allows the user to be located remotely from the centralized database. As mentioned previously, the centralized database contains a variety of security features to prevent sensitive detailed data from being viewed or accessed by users without the proper security clearance. Procedures may be used to act on the data to generate results of various inquires. The procedures may be part of a standard set of calculations or may be developed and generated by the user. The results of the action by the procedures may be displayed to the user on a number of output means. Examples of suitable output means comprise computer terminals, 307, personal computers (PCs), 306, printers, 310, LED displays, personal digital assistants (PDAs), 308, cellular phones, 309, interactive displays, and the like.

FIG. 4 depicts an illustrative computer system pertaining to various embodiments of the present invention. In some embodiments, the computer system comprises a server 401, display, 402, one or more input interfaces, 403, communications interface, 406, and one or more output interfaces, 404, all conventionally coupled by one or more buses, 405. The server, 401, comprises one or more processors (not shown) and one or more memory modules, 412. The input interfaces, 403, may comprise a keyboard, 408, and a mouse, 409. The output interface, 404, may comprise a printer, 410. The communications interface, 406, is a network interface that allows the computer system to communicate via a wireless or hardwired network, 407, as previously described. The communications interface, 407, may be coupled to a transmission medium, 411, such as a network transmission line, for example, twisted pair, coaxial cable, fiber optic cable, and the like. In another embodiment, the communications interface, 411, provides a wireless interface, that is, the communication interface, 411 uses a wireless transmission medium. Examples of other devices that may be used to access the computer system via communications interface, 406, comprise cell phones, PDAs, personal computers, and the like (not shown).

The memory modules, 412, generally comprises different modalities, illustratively semiconductor memory, such as random access memory (RAM), and disk drives as well as others. In various embodiments, the memory modules, 412, store an operating system, 413, collected and aggregated data, 414, instructions, 415, applications, 416, and procedures, 417.

In various embodiments, the specific software instructions, data structures and data that implement various embodiments of the present invention are typically incorporated in the server, 401. Generally, an embodiment of the present invention is tangibly embodied in a computer readable medium, for example, the memory and is comprised of instructions, applications, and procedures which, when executed by the processor, causes the computer system to utilize the present invention, for example, the collection, aggregation, and analysis of data, using the aggregated data to improve committed guarantees, displaying the results of the analyses, and the like. The memory may store the software instructions, data structures, and data for any of the operating system, the data collection application, the data aggregation application, the data analysis procedures, and the like in semiconductor memory, in disk memory, or a combination thereof.

The operating system may be implemented by any conventional operating system comprising Windows® (Registered trademark of Microsoft Corporation), Unix® (Registered trademark of the Open Group in the United States and other countries), Mac OS® (Registered trademark of Apple Computer, Inc.), Linux® (Registered trademark of Linus Torvalds), as well as others not explicitly listed herein.

In various embodiments, the present invention may be implemented as a method, system, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof. The term “article of manufacture” (or alternatively, “computer program product”) as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier or media. In addition, the software in which various embodiments are implemented may be accessible through the transmission medium, for example, from a server over the network. The article of manufacture in which the code is implemented also encompasses transmission media, such as the network transmission line and wireless transmission media. Thus the article of manufacture also comprises the medium in which the code is embedded. Those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the present invention.

The exemplary computer system illustrated in FIG. 4 is not intended to limit the present invention. Other alternative hardware environments may be used without departing from the scope of the present invention.

The foregoing descriptions of exemplary embodiments of the present invention have been presented for the purpose of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications, embodiments, and variations are possible in light of the above teaching. 

1. A computer implemented method for improving a System Performance Guarantee commitment, comprising: using a model of benchmark system response and system performance to generate an expected result of system performance based on said model (200); selecting a coefficient ranging between 0 and 1 (201); calculating the System Performance Guarantee commitment by multiplying said expected result from said model by said coefficient (202); monitoring the system performance and comparing said system performance to the System Performance Guarantee commitment (204-206).
 2. The method of claim 1 wherein said system comprises an energy system.
 3. The method of claim 2 wherein said system comprises an energy usage system an energy storage system, an energy management system, or an energy generation system.
 4. The method of claim 3 wherein said energy generation system comprises a renewable energy generation system.
 5. The method of claim 4 wherein said renewable energy generation system comprises a solar energy generation system, a wind turbine energy generation system, a tidal energy generation system, a geothermal energy generation system, or a waste-to-energy system.
 6. A system for improving a System Performance Guarantee commitment, comprising: one or more Energy Systems (300); sensors contained within said Energy Systems to monitor said Energy Systems settings and performance attributes data; sensors associated with said Energy Systems to measure environmental conditions data (302); a local communications device for communicating said Energy Systems settings and performance attributes data and said environmental conditions data onto a network (303); a network capable of transmitting said Energy Systems settings and performance attributes data and said environmental conditions data (304); a centralized database capable of receiving and storing said Energy Systems settings and performance attributes data and said environmental conditions data (505); a user interface for interacting with said centralized database (306-309, 403, 408, 409); a computer readable medium containing procedures for acting upon said Energy Systems settings and performance attributes data and said environmental conditions data (412-417); and display output devices for displaying the results of said procedure action upon said Energy Systems settings and performance attributes data and said environmental conditions data (306-310, 404, 410).
 7. The system of claim 6 wherein said one or more Energy Systems comprise at least one Energy System selected from the group consisting of an energy usage system, an energy storage system, an energy management system, and an energy generation system.
 8. The system of claim 7 wherein said energy generation system comprises a renewable energy generation system.
 9. The system of claim 8 wherein said renewable energy generation system comprises a solar energy generation system, a wind turbine energy generation system, a tidal energy generation system, a geothermal energy generation system, or a waste to energy system.
 10. The system of claim 9 wherein said procedures comprise: procedures for modeling benchmark system response and system performance to generate an expected result of system performance; and procedures for calculating the System Performance Guarantee commitment by multiplying said expected result from said model by a coefficient between 0 and
 1. 